Multi-agent reinforcement learning (MARL) has achieved significant progress in solving complex multi-player games through self-play. However, training effective adversarial policies requires millions of experience samples and substantial computational resources. Moreover, these policies lack interpretability, hindering their practical deployment. Recently, researchers have successfully leveraged Large Language Models (LLMs) to generate programmatic policies for single-agent tasks, transforming neural network-based policies into interpretable rule-based code with high execution efficiency. Inspired by this, we propose PolicyEvolve, a general framework for generating programmatic policies in multi-player games. PolicyEvolve significantly reduces reliance on manually crafted policy code, achieving high-performance policies with minimal environmental interactions. The framework comprises four modules: Global Pool, Local Pool, Policy Planner, and Trajectory Critic. The Global Pool preserves elite policies accumulated during iterative training. The Local Pool stores temporary policies for the current iteration; only sufficiently high-performing policies from this pool are promoted to the Global Pool. The Policy Planner serves as the core policy generation module. It samples the top three policies from the Global Pool, generates an initial policy for the current iteration based on environmental information, and refines this policy using feedback from the Trajectory Critic. Refined policies are then deposited into the Local Pool. This iterative process continues until the policy achieves a sufficiently high average win rate against the Global Pool, at which point it is integrated into the Global Pool. The Trajectory Critic analyzes interaction data from the current policy, identifies vulnerabilities, and proposes directional improvements to guide the Policy Planner

Multi-human multi-robot teams combine the complementary strengths of humans and robots to tackle complex tasks across diverse applications. However, the inherent heterogeneity of these teams presents significant challenges in initial task allocation (ITA), which involves assigning the most suitable tasks to each team member based on their individual capabilities before task execution. While current learning-based methods have shown promising results, they are often computationally expensive to train, and lack the flexibility to incorporate user preferences in multi-objective optimization and adapt to last-minute changes in real-world dynamic environments. To address these issues, we propose REBEL, an LLM-based ITA framework that integrates rule-based and experience-enhanced learning. By leveraging Retrieval-Augmented Generation, REBEL dynamically retrieves relevant rules and past experiences, enhancing reasoning efficiency. Additionally, REBEL can complement pre-trained RL-based ITA policies, improving situational awareness and overall team performance. Extensive experiments validate the effectiveness of our approach across various settings. More details are available at https://sites.google.com/view/ita-rebel .

Join the Cox family of businesses and make your mark today! About Cox CommunicationsCox Communications is the largest private telecom company in America, serving six million homes and businesses. That's a lot, but we also proudly serve our employees. Our benefits and our award-winning culture are just two of the things that make Cox a coveted place to work. If you're interested in bringing people closer through broadband, smart home tech and more, join Cox Communications today! About CoxCox empowers employees to build a better future and has been doing so for over 120 years. With exciting investments and innovations across transportation, communications, cleantech and healthcare, our family of businesses – which includes Cox Automotive and Cox Communications – is forging a better future for us all. Ready to make your mark?

Role requiring'No experience data provided' months of experience in None Combine two of the fastest-growing fields on the planet with a culture of performance, collaboration and opportunity and this is what you get.

Role requiring'No experience data provided' months of experience in San Francisco We are a startup within an enterpise business and have huge growth plans! Our product relies on Computer Vision to make it easier for customers to choose between different product offerings. We are headquartered in the Bay Area but have engineers throughout the country! With a remote-first culture, we strongly believe in collobaration via Microsoft Teams. Our software is used daily by millions of customers globally and we are still gaining new customers, we have exciting plans for the future!

Role requiring'No experience data provided' months of experience in Jacksonville GENERAL REQUIREMENTS: • Experience with unit testing, release procedures, coding design and documentation protocol as well as change management procedures • Proficiency using React, Material, TypeScript, Webpack, rollup, Redux, boilerplate; • Demonstrated organizational, analytical and interpersonal skills • Flexible team player • Ability to manage tasks independently and take ownership of responsibilities • Ability to learn from mistakes and apply constructive feedback to improve performance • Must demonstrate initiative and effective independent decision-making skills • Ability to communicate technical information clearly and articulately • Ability to adapt to a rapidly changing environment • In-depth understanding of the systems development life cycle o Database knowledge in Mongo DB; REDIS and Jenkins is a plus – but not required. Will be part of a highly motivated and skilled MERN Stack Team that manages central tooling and maintenance of the technology stack. The person will be part of an highly skilled team that develops UI boilerplate code and other frameworks utilized across projects of the organization. The core platform includes best-in-class CRM with responsive and seamless UI that serves 4000 plus individual's with 24 7 uptime. Self-managing (Agile) – the team leads in the creation of best-in-class frameworks and reusable tools for the organization and is a treat to work in.

GENERAL REQUIREMENTS: • Experience with unit testing, release procedures, coding design and documentation protocol as well as change management procedures • Proficiency using MERN and MEAN Scaffolding tools • Demonstrated organizational, analytical and interpersonal skills • Flexible team player • Ability to manage tasks independently and take ownership of responsibilities • Ability to learn from mistakes and apply constructive feedback to improve performance • Must demonstrate initiative and effective independent decision-making skills • Ability to communicate technical information clearly and articulately • Ability to adapt to a rapidly changing environment • In-depth understanding of the systems development life cycle • Proficiency programming in more than one object-oriented programming language; React.Js, Node.JS, JavaScript, and HTML • Proficiency with HTML, CSS, SASS, JavaScript/jQuery, local storage, and cross-browser compatibility are required • May include database knowledge in MongoDB • Experience with modern web/UI development tools and techniques: Node, Webpack, Grunt/Gulp, GIT, Axios, Jest • Client-side templating: mustache.js,

A $1000 CAD (or equivalent in your country's currency) work from home allowance to make your home setup perfect for you A lifestyle spending account for employees to receive reimbursement for eligible expenses related to wellness, lifestyle and productivity $2500 CAD (or equivalent in your country's currency) per year At BenchSci, we're committed to cultivating an inspiring, inclusive, and equitable work environment for high performing, ego-free, self-starting individuals with a growth mindset, who enjoy the challenge of solving hard problems. We recognize that everyone here is a person first and an employee second. We want people to feel cared for and supported to bring the best versions of themselves to work and help the company achieve its mission. We believe culture is critical to success and invest accordingly. We live and promote our FASTT values of Focused, Advancement with Speed, Tenacity, and Transparency. We work hard to maintain an engaging, supportive environment where everyone can do their best work. To learn more, read our culture deck.

At Jasper, we believe in pay transparency and are committed to providing our employees and candidates with access to information about our compensation practices. The expected base salary range at offer for this role is $197,000- $225,000. Compensation may vary based on relevant experience, skills, competencies and certifications.

Role requiring'No experience data provided' months of experience in None